Systems and methods for non-obstacle area detection

ABSTRACT

A method performed by an electronic device is described. The method includes generating a depth map of a scene external to a vehicle. The method also includes performing first processing in a first direction of a depth map to determine a first non-obstacle estimation of the scene. The method also includes performing second processing in a second direction of the depth map to determine a second non-obstacle estimation of the scene. The method further includes combining the first non-obstacle estimation and the second non-obstacle estimation to determine a non-obstacle map of the scene. The combining includes combining comprises selectively using a first reliability map of the first processing and/or a second reliability map of the second processing The method additionally includes navigating the vehicle using the non-obstacle map.

RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.14/858,471, filed Sep. 18, 2015, for “SYSTEMS AND METHODS FORNON-OBSTACLE AREA DETECTION,” which is assigned to the assignee hereofand hereby expressly incorporated by reference herein.

FIELD OF DISCLOSURE

The present disclosure relates generally to electronic devices. Morespecifically, the present disclosure relates to systems and methods fornon-obstacle area detection.

BACKGROUND

In the last several decades, the use of electronic devices has becomecommon. In particular, advances in electronic technology have reducedthe cost of increasingly complex and useful electronic devices. Costreduction and consumer demand have proliferated the use of electronicdevices such that they are practically ubiquitous in modern society. Asthe use of electronic devices has expanded, so has the demand for newand improved features of electronic devices. More specifically,electronic devices that perform new functions and/or that performfunctions faster, more efficiently or with higher quality are oftensought after.

Some electronic devices (e.g., cameras, video camcorders, digitalcameras, cellular phones, smart phones, computers, televisions, etc.)capture and/or utilize images. For example, a smartphone may captureand/or process still and/or video images. In automotive and autonomousvehicle applications, obstacle detection may be performed by processingan image. Processing images may demand a relatively large amount oftime, memory and energy resources. The resources demanded may vary inaccordance with the complexity of the processing.

It may be difficult to implement some complex processing tasks. Forexample, some platforms may have limited processing, memory and/orenergy resources. Furthermore, some applications may be time sensitive.As can be observed from this discussion, systems and methods thatimprove image processing may be beneficial.

SUMMARY

A method performed by an electronic device is described. The methodincludes performing vertical processing of a depth map to determine avertical non-obstacle estimation. The method also includes performinghorizontal processing of the depth map to determine a horizontalnon-obstacle estimation. The method further includes combining thevertical non-obstacle estimation and the horizontal non-obstacleestimation. The method additionally includes generating a non-obstaclemap based on the combination of the vertical and horizontal non-obstacleestimations.

Performing vertical processing may include dividing the depth map intosegments. At least one segment may include a number of pixels in acolumn. Linear model parameters may be estimated for at least onesegment to determine the vertical non-obstacle estimation. A verticalreliability map that includes a reliability value for the verticalnon-obstacle estimation may be generated.

Determining the vertical reliability map may include determining asegment fitting error for a given segment based on a difference betweenthe estimated linear model parameters and pre-determined linear modelparameters. A reliability value for the given segment may be determinedby comparing the segment fitting error to a vertical estimationthreshold. The reliability value for the given segment may be applied toat least one pixel in the given segment.

The pre-determined linear model parameters may be selected from among aplurality of road condition models. The plurality of road conditionmodels may have a corresponding set of linear model parameters.

Performing horizontal processing may include obtaining a depth histogramfor at least one row of pixels of the depth map. A terrain line may bedetermined from the depth histogram. A horizontal non-obstacleestimation may be determined based on a distance of a depth value of atleast one pixel from the terrain line. A horizontal reliability map maybe generated that includes a reliability value for the horizontalnon-obstacle estimation.

Generating the horizontal reliability map may include determiningwhether the depth value of a given pixel is within a range of a mode ofthe depth histogram. The given pixel may have a high reliability valuewhen the depth value of the given pixel is within the range of the modeof the depth histogram.

Combining the vertical non-obstacle estimation and the horizontalnon-obstacle estimation may include performing both the verticalprocessing and the horizontal processing in parallel. The verticalnon-obstacle estimation and the horizontal non-obstacle estimation maybe merged based on a vertical reliability map and a horizontalreliability map.

A given pixel may be identified as a non-obstacle area in thenon-obstacle map where both the vertical reliability map and thehorizontal reliability map are characterized by a high reliability valuefor the given pixel. A given pixel may be identified as an obstacle areain the non-obstacle map where at least one of the vertical reliabilitymap or the horizontal reliability map are characterized by a lowreliability value for the given pixel. A given pixel may be identifiedas a non-obstacle area or obstacle area in the non-obstacle map based ona coordinate of the given pixel where the vertical reliability map andthe horizontal reliability map are characterized by differentreliability values for the given pixel.

Combining the vertical non-obstacle estimation and the horizontalnon-obstacle estimation may include performing vertical processing ofthe depth map. A vertical reliability map may be obtained based on amodel fitting confidence. Reliable regions of the vertical reliabilitymap may be identified as non-obstacle areas. Horizontal processing maybe performed on unreliable regions of the vertical reliability map todetermine whether the unreliable regions are non-obstacle areas.

Combining the vertical non-obstacle estimation and the horizontalnon-obstacle estimation may include performing horizontal processing ofthe depth map. A horizontal reliability map may be obtained based on adepth histogram distance. Reliable regions of the horizontal reliabilitymap may be identified as non-obstacle areas. Vertical processing onunreliable regions of the horizontal reliability map may be performed todetermine whether the unreliable regions are non-obstacle areas.

The non-obstacle map may be used in identifying a region of interestused by at least one of an object detection algorithm or a lanedetection algorithm.

An electronic device is also described. The electronic device isconfigured to perform vertical processing of a depth map to determine avertical non-obstacle estimation. The electronic device is alsoconfigured to perform horizontal processing of the depth map todetermine a horizontal non-obstacle estimation. The electronic device isfurther configured to combine the vertical non-obstacle estimation andthe horizontal non-obstacle estimation. The electronic device isadditionally configured to generate a non-obstacle map based on thecombination of the vertical and horizontal non-obstacle estimations.

An apparatus is also described. The apparatus includes means forperforming vertical processing of a depth map to determine a verticalnon-obstacle estimation. The apparatus also includes means forperforming horizontal processing of the depth map to determine ahorizontal non-obstacle estimation. The apparatus further includes meansfor combining the vertical non-obstacle estimation and the horizontalnon-obstacle estimation. The apparatus additionally includes means forgenerating a non-obstacle map based on the combination of the verticaland horizontal non-obstacle estimations.

A computer-program product is also described. The computer-programproduct includes a non-transitory tangible computer-readable mediumhaving instructions thereon. The instructions include code for causingan electronic device to perform vertical processing of a depth map todetermine a vertical non-obstacle estimation. The instructions alsoinclude code for causing the electronic device to perform horizontalprocessing of the depth map to determine a horizontal non-obstacleestimation. The instructions further include code for causing theelectronic device to combine the vertical non-obstacle estimation andthe horizontal non-obstacle estimation. The instructions additionallyinclude code for causing the electronic device to generate anon-obstacle map based on the combination of the vertical and horizontalnon-obstacle estimations.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating an electronic device configuredto perform non-obstacle area detection;

FIG. 2 is a block diagram illustrating a non-obstacle determinationmodule;

FIG. 3 is a flow diagram illustrating a method for performingnon-obstacle area detection;

FIGS. 4A-4D illustrate an example of a vertical processing of a depthmap;

FIG. 5 is a flow diagram illustrating a method for performingnon-obstacle area detection using vertical processing;

FIGS. 6A-6D illustrate an example of a horizontal processing of a depthmap;

FIG. 7 is a flow diagram illustrating a method for performingnon-obstacle area detection using horizontal processing;

FIG. 8 is a flow diagram illustrating a method for combining a verticalnon-obstacle estimation and a horizontal non-obstacle estimation todetermine a non-obstacle map;

FIG. 9 is a flow diagram illustrating another method for combining avertical non-obstacle estimation and a horizontal non-obstacleestimation to determine a non-obstacle map;

FIG. 10 is a flow diagram illustrating yet another method for combininga vertical non-obstacle estimation and a horizontal non-obstacleestimation to determine a non-obstacle map;

FIGS. 11A-11B illustrate an example of determining non-obstacle areas inan image;

FIGS. 12A-12B illustrate another example of determining non-obstacleareas in an image;

FIGS. 13A-13B illustrate an example of determining distance from anon-obstacle area;

FIG. 14 illustrates an example of using the non-obstacle map to identifya region of interest (ROI) used by an object detection algorithm;

FIG. 15 illustrates an example of using the non-obstacle map to identifya ROI used by a lane detection algorithm;

FIG. 16 is a flow diagram illustrating a method for classifying a roadbased on estimated linear model parameters; and

FIG. 17 illustrates certain components that may be included within anelectronic device.

DETAILED DESCRIPTION

In many applications, it is advantageous to identify non-object areaswithin a region or environment for obstacle avoidance. For example, withadvanced driver assistance systems (ADAS), it is important to identifythe drivable area in front of the car for obstacle avoidance. Anotherscenario in which obstacle avoidance is important is vehicularautomation in which an autonomous vehicle (e.g., an unmanned aerialvehicle (UAV) or autonomous automobile) senses its environment andnavigates without human input.

Typically the problem of obstacle avoidance is looked at from the otherdirection, where objects of interest (e.g., pedestrians, cars, bicycles)are detected and may be identified via object detection/recognitionalgorithms and alerts are provided to the driver for taking precaution.For example, obstacle detection may be utilized to detect and identifytraffic signs (e.g., speed limit signs, stop signs, street signs, etc.).However, this approach may be slow and inaccurate. Furthermore all theobject classes have to be trained beforehand, which makes it difficultto ad din new object classes.

In the systems and methods described herein, instead of identifyingobjects, regions are identified that are free of obstacles or objects.These regions may be referred to as moveable areas. In other words,these three dimensional regions are non-obstacle areas in which movementis possible. For example, a non-obstacle area may be identified on aroad. Upon identifying non-obstacle areas, various other applicationssuch as road profiling, speed control, faster and more reliable object,and lane detection may be performed using the non-obstacle area.

The systems and methods described herein may be used to identify anon-obstacle area by analyzing a depth map. In an implementation, thedepth map may be generated from a stereo image pair. In a road scenario,open space in the front may be mapped to a linear model that describesthe road in the depth domain. By using the depth of road segments,linear model parameters may be estimated. The estimated linear modelparameters may be compared with prior (e.g., pre-determined) modelparameters. If fitting is achieved, then the segments are declared to bepart of the open area. If a stereo image pair is not available, methodssuch as structure from motion can be used to obtain a depth map by usinga mono camera input sequence.

The systems and methods described herein provide for the determinationof a non-obstacle map based on an intelligent combination of verticalprocessing and horizontal processing of one or more images. Systems andmethods of performing identifying a non-obstacle area are explained ingreater detail below.

FIG. 1 is a block diagram illustrating an electronic device 102configured to perform non-obstacle area detection. The electronic device102 may also be referred to as a wireless communication device, a mobiledevice, mobile station, subscriber station, client, client station, userequipment (UE), remote station, access terminal, mobile terminal,terminal, user terminal, subscriber unit, etc. Examples of electronicdevices include laptop or desktop computers, cellular phones, smartphones, wireless modems, e-readers, tablet devices, gaming systems,robots, aircraft, unmanned aerial vehicles (UAVs), automobiles, etc.Some of these devices may operate in accordance with one or moreindustry standards.

In many scenarios, the electronic device 102 may use a non-obstacle map126 of a scene. In one example, a smartphone may generate a non-obstaclemap 126 of a scene to identify unoccupied space. In another example, anautomobile may include an advanced driver assistance system (ADAS) thatmay use a non-obstacle map 126 to regulate speed, steering, parking,etc., of the automobile based on detected traffic signs, signals and/orother objects. In another example, an unmanned aerial vehicle (UAV) maygenerate a non-obstacle map 126 from video recorded while in flight, maynavigate based on detected objects (e.g., buildings, signs, people,packages, etc.), may pick up and/or deliver a detected package, etc.Many other examples may be implemented in accordance with the systemsand methods disclosed herein. For instance, the systems and methoddisclosed herein could be implemented in a robot that performs one ormore actions (e.g., fetching something, assembling something, searchingfor an item, etc.) based on one or more objects detected using thenon-obstacle map 126.

An electronic device 102 may include one or more cameras. A camera mayinclude an image sensor 104 and an optical system 108 (e.g., lenses)that focuses images of objects that are located within the field of viewof the optical system 108 onto the image sensor 104. An electronicdevice 102 may also include a camera software application and a displayscreen. When the camera application is running, images 114 of objectsthat are located within the field of view of the optical system 108 maybe recorded by the image sensor 104. These images 114 may be stored in amemory buffer 112. In some implementations, the camera may be separatefrom the electronic device 102 and the electronic device 102 may receiveimage data from one or more cameras external to the electronic device102.

Although the present systems and methods are described in terms ofcaptured images 114, the techniques discussed herein may be used on anydigital image. Therefore, the terms video frame and digital image may beused interchangeably herein.

In many applications, it is beneficial for an electronic device 102 toidentify areas that are free of obstacles. For example, in the case ofADAS, it is important to identify the drivable area in front of the carfor obstacle avoidance. In some approaches, this problem is looked atfrom the other direction. In these approaches, objects of interest(e.g., pedestrians, cars, bicycles) are identified via object detectionand recognition algorithms and alerts are provided to the driver fortaking precaution.

Other approaches may determine non-obstacle areas by performing one ofeither vertical processing or horizontal processing of an image 114.Each of the vertical processing and horizontal processing may estimatenon-obstacle areas in an image 114. However, each approach haslimitations when performed independently.

Horizontal processing may give an incorrect non-obstacle estimation whena road is slanted. Vertical processing may perform segmentation on theimage 114. If the segments are large, then fitting is more reliable.However parts of objects could be included as non-obstacle (i.e.,non-obstacle) areas as well. For example, using vertical processing, thebottom and top of cars or pedestrians may be incorrectly identified as anon-obstacle area. Also, sidewalks could be incorrectly identified as anon-obstacle area with the vertical processing approach. However, ifsegments are small, an inaccurate detection of non-obstacle area mayoccur.

The systems and methods described herein provide for the determinationof a non-obstacle map 126 based on an intelligent combination ofvertical processing and horizontal processing of one or more images 114.In the described systems and methods, instead of identifying objects, anon-obstacle map 126 may be determined by combining vertical processingand horizontal processing of a depth map. In one implementation, thedepth map may be obtained from one or more images. In anotherimplementation, the depth map may be obtained from a depth dataacquisition process (e.g., LIDAR).

In an implementation, the electronic device 102 may include anon-obstacle determination module 116 for determining a non-obstacle map126. The non-obstacle determination module 116 may include a depth mapgenerator 118 for obtaining a depth map. The non-obstacle determinationmodule 116 may also include a vertical estimation module 120 forperforming vertical processing of the depth map to determine a verticalnon-obstacle estimation. The non-obstacle determination module 116 mayfurther include a horizontal estimation module 122 for performinghorizontal processing of the depth map to determine a horizontalnon-obstacle estimation.

An estimation combiner 124 may combine the vertical non-obstacleestimation and the horizontal non-obstacle estimation to determine anon-obstacle map 126. In an implementation, the estimation combiner 124may intelligently combine the vertical non-obstacle estimation and thehorizontal non-obstacle estimation based on reliability valuesassociated with the estimations. The non-obstacle map 126 may indicatewhich areas of the one or more images 114 are non-obstacle areas andwhich areas are obstacle areas. More detail on generating thenon-obstacle map 126 is given in connection with FIG. 2.

In an example, the non-obstacle map 126 may include non-obstacle areasthat are free of obstacles (e.g., objects) on the roads. By combiningthe vertical processing and horizontal processing of an image 114, falsedetections may be eliminated. Furthermore, a combined approach mayprovide a more reliable non-obstacle estimation.

The described systems and methods may be used to speed up various otherapplications. For example, the electronic device 102 may use thenon-obstacle map 126 for other applications such as object detection,scene understanding, road profiling, and lane detection. The describedsystems and methods may also solve the problem of road curb detectionthat current lane detection algorithms cannot handle. The describedsystems and methods may further help in autonomous driving, where thespeed is adjusted based on the terrain of the road.

FIG. 2 is a block diagram illustrating a non-obstacle determinationmodule 216. The non-obstacle determination module 216 may be implementedwithin an electronic or wireless device. The non-obstacle determinationmodule 216 may include a depth map generator 218, a vertical estimationmodule 220, a horizontal estimation module 222 and an estimationcombiner 224. The non-obstacle determination module 216, depth mapgenerator 218, a vertical estimation module 220, a horizontal estimationmodule 222 and an estimation combiner 224 may be configurations of thenon-obstacle determination module 116, depth map generator 118, avertical estimation module 120, a horizontal estimation module 122 andan estimation combiner 124 described above in connection with FIG. 1.

The depth map generator 218 may receive one or more images 214. In aconfiguration, the one or more images 214 may be obtained via a camera(e.g., an image sensor 104 and optical system 108), as described inconnection with FIG. 1. In an implementation, the depth map generator218 may receive a stereo image pair. In another implementation, the oneor more images 214 may come from a video mono image. The image 214 maybe a digital image made up of pixels.

The depth map generator 218 may obtain a depth map 230 from the one ormore images 214. The depth map 230 may include pixels 232 correspondingto the pixels of the one or more images 214. Each pixel 232 in the depthmap 230 has a depth value 234. The depth value 234 indicates a distanceof the pixel 232 relative to the camera. For example, a pixel 232 with ahigher depth value 234 may indicate a closer proximity to the camerathan a pixel 232 with a lower depth value 234. An example of a depth map230 is discussed in connection with FIGS. 4A-4D.

It should be noted that the algorithm described herein does not requirea depth value 234 for each pixel 232. For example, the depth map 230 mayhave holes. The depth value 234 of these holes may be filled in beforestarting processing.

In the case of a stereo image pair, the depth map generator 218 may findthe correspondence between pixels 232 to estimate a disparity. From thedisparity, the depth map generator 218 may determine the depth value 234for each pixel 232. Alternatively, with a video mono image, the depthmap generator 218 may determine the depth value 234 for each pixel 232based on the motion of the video.

The depth map 230 may be provided to the vertical estimation module 220and the horizontal estimation module 222. The vertical estimation module220 may perform vertical processing of the depth map 230 to determine avertical non-obstacle estimation 242. The horizontal estimation module222 may perform horizontal processing of the depth map 230 to determinea horizontal non-obstacle estimation 258.

The vertical estimation module 220 may divide the depth map 230 intosegments 236. Each segment 236 may include a number of pixels 232 in acolumn of the depth map 230. In other words, the vertical estimationmodule 220 may process vertical segments 236 of the depth map 230. Asegment 236 may be a portion of a column of pixels 232 in the depth map230. For example, the segment 236 may be a vector of 10-15 pixels 232.The segments 236 may or may not overlap.

The vertical estimation module 220 may estimate linear model parametersfor each segment 236 to determine the vertical non-obstacle estimation242. Each segment 236 in the vertical direction may be estimated via alinear model. The linear model may be expressed asy=p ₁ x+p ₂,  (1)where y is the depth value 234 of a pixel 232, x is the coordinate ofthe pixel 232, and p₁ and p₂ are the estimated linear model parameters238. If the estimation error is less than a vertical estimationthreshold 246 and the linear fit has certain slope, it may be labelledas valid free space (a non-obstacle area 266). In an implementation, aslope greater than 0 indicates non-obstacle, whereas a slope that isless than or equal to 0 indicates an object or obstacle. An example ofthis approach is discussed in connection with FIGS. 4A-4D.

In matrix form, the linear model of Equation (1) is given by Equation(2).

$\begin{matrix}{\begin{bmatrix}y_{1} \\y_{2} \\y_{3} \\\vdots \\y_{n}\end{bmatrix} = {\begin{bmatrix}x_{1}^{1} \\x_{2}^{1} \\x_{3}^{1} \\\vdots \\x_{n}^{1}\end{bmatrix} \times \begin{bmatrix}p_{1} \\p_{2}\end{bmatrix}}} & (2)\end{matrix}$

In Equation (2), y is an n-by-1 vector of depth values 234, x is then-by-2 design matrix (i.e., a matrix of n rows, 2 columns) for the modelcoordinates in the vertical dimension, and p₁ and p₂ are the estimatedlinear model parameters 238. The value of n corresponds to the number ofpixels 232 in the depth map segment 236.

In one approach, p₁, and p₂ may be solved using a least-squaressolution. In this approach, p is a vector of the unknown coefficientsp₁, and p₂. The normal equations are given by(X ^(T) X)p=X ^(T) y  (3)where X^(T) is the transpose of the design matrix X. Solving for p inEquation (4) results in the estimated linear model parameters 238 (i.e.,p₁ and p₂).p=(X ^(T) X)⁻¹ X ^(T) y  (4)

It should be noted that in an optimized implementation, the matrixinversion term (X^(T)X)⁻¹X^(T) is fixed based on the location (i.e., they-coordinate) and could be pre-calculated based on location and stored.Also, a piecewise linear model could estimate dips and bumps in asurface, with variation in p₁, p₂ and segment length. Furthermore, theprimary complexity of this approach is due to matrix multiplication oftwo vectors of size n in the above Equation (2). In an example, n may be10 or the chosen piecewise length instead of 10.

The vertical estimation module 220 may determine a vertical reliabilitymap 248 a reliability value 250 for the vertical non-obstacle estimation242. In an implementation, the vertical estimation module 220 maydetermine a segment fitting error 244 for each segment 236 based on thedifference between the estimated linear model parameters 238 andpre-determined linear model parameters 240. The predetermined modelparameters may be referred to as [pM1, pM2]. The vertical estimationmodule 220 may check how close the predetermined model parameters are tothe estimated parameters [p₁, p₂] by comparing to a threshold (TH1). Forexample, |p₁−pM1|<TH1.

The vertical estimation threshold 246 may determine if the estimatedlinear model parameters 238 fit into the trained model parameters. Thepre-determined linear model parameters 240 may be selected from among aplurality of road condition models. Each of the plurality of roadcondition models may have a corresponding set of linear model parameters240. Examples of the road condition models include a flat plane, hill,valley, etc. The pre-determined linear model parameters 240 may bedetermined by training.

The vertical estimation module 220 may determine an estimated depthvalue (ŷ_(i)) using the estimated linear model parameters 238 accordingto Equation (5), where ŷ_(i) is the estimated depth value at the ithlocation.ŷ _(i) ≡p ₁ x _(i) +p ₂  (5)

A depth estimation error (e_(i)) with the estimated linear modelparameters 238 may be determined according to Equation (6), where y_(i)is the depth value 234 at the ith location.e ₁ ≡y _(i) −ŷ _(i)  (6)

The segment fitting error 244 (s²) may be determined according toEquation (7), where n is the length of the segment 236.

$\begin{matrix}{s^{2} = {\sum\limits_{i = 1}^{n}\frac{e_{i}^{2}}{n - 2}}} & (7)\end{matrix}$

The segment fitting error 244 may be used as a reliability metric forvertical processing. The vertical estimation module 220 may determinethe vertical reliability value 250 for each segment 236 by comparing thesegment fitting error 244 of each segment 236 to a vertical estimationthreshold 246.

The vertical estimation threshold 246 may be based on the pre-determinedlinear model parameters 240. The vertical estimation threshold 246 mayvary depending on which pre-determined linear model parameters 240 areused. For example, the vertical estimation threshold 246 may have onevalue if pre-determined linear model parameters 240 for a flat road areused. The vertical estimation threshold 246 may have a different valueif pre-determined linear model parameters 240 for a hill are used.

The vertical estimation module 220 may check the absolute value of thedifference between the segment fitting error 244 and the verticalestimation threshold 246. A given segment 236 may have a high verticalreliability value 250 when the segment fitting error 244 for the givensegment 236 is less than the vertical estimation threshold 246.Conversely, the given segment 236 may have a low vertical reliabilityvalue 250 when the segment fitting error 244 for the given segment 236is greater than or equal to the vertical estimation threshold 246.Therefore, if the difference between the segment fitting error 244 andthe vertical estimation threshold 246 is small, then the segment 236 isconsidered as part of a non-obstacle area 266. If the difference islarge, then the segment 236 is not considered as part of a non-obstaclearea 266.

Upon determining the reliability value 250 for a given segment 236, thevertical estimation module 220 may threshold the vertical reliabilitymap 248 to get a binary map. The reliability value 250 for a givensegment 236 may be compared with a threshold to determine whether thesegment 236 is a non-object area (e.g., movable) or an object area(e.g., non-moveable area). The vertical reliability map 248 may includethe reliability values 250 of the pixels 232 in the depth map 230.Therefore, the vertical reliability map 248 is a map that has a fittingconfidence per depth pixel 232 as well as per segment 236.

The horizontal estimation module 222 may perform horizontal processingof the depth map 230 to determine a horizontal non-obstacle estimation258. The horizontal processing may include obtaining a depth histogram252 for each row of pixels 232 of the depth map 230. An example of thedepth histogram 252 is described in connection with FIGS. 6A-6D. Thedepth histogram 252 may also be referred to as a disparity histogram.

In an implementation, the depth histogram 252 may be obtained byobtaining a histogram for each row in the depth map 230. For example,for a row of pixels 232 from the depth map 230, a histogram may begenerated for the depth values 234 corresponding to the pixels 232 inthe row.

The horizontal estimation module 222 may determine a terrain line 254from the depth histogram 252. The y-coordinate of the end points of theterrain line 254 determine the closest and farthest free space distance.

The terrain line 254 may be determined using a line extraction approach.A Hough transform may be applied on the depth histogram 252. The Houghtransform may extract multiple line segments from the depth histogram252. For example, the Hough transform may generate 10-20 small lines.The line segments may be merged to form the terrain line 254. In animplementation, the angle of the terrain line 254 may be limited (e.g.,between −20 degrees and −40 degrees) based on slope characteristics ofnon-obstacle space.

The horizontal estimation module 222 may determine the horizontalnon-obstacle estimation 258 for each pixel 232 based on the distance ofthe depth value 234 of each pixel 232 from the terrain line 254. A pixel232 that has a depth value 234 that lies within a horizontal estimationthreshold 260 of the terrain line 254 may be identified as anon-obstacle area 266. For example, if the depth value 234 of a pixel232 lies within a certain disparity range of the terrain line 254, thenthat pixel 232 may be labeled as a non-obstacle area 266. If the depthvalue 234 of a pixel 232 lies outside the certain disparity range of theterrain line 254, then that pixel 232 may be labeled as an obstacle area268.

The horizontal estimation module 222 may also determine a horizontalreliability map 262 based on a depth histogram 252 distance. Thehorizontal reliability map 262 may include a reliability value 264 forthe horizontal non-obstacle estimation 258 of each pixel 232.

The distance to the mode 256 of the depth histogram 252 may be used as areliability metric for the horizontal processing. The horizontalestimation module 222 may determine whether the depth value 234 of eachpixel 232 is within a reliability threshold 261 of the mode 256 of thedepth histogram 252. A given pixel 232 may have a high reliability value264 when the depth value 234 of the given pixel 232 is within thereliability threshold 261 of the mode 256 of the depth histogram 252.

The estimation combiner 224 may combine the vertical non-obstacleestimation 242 and the horizontal non-obstacle estimation 258 todetermine the non-obstacle map 226. This combination may be based on thevertical reliability values 250 and the horizontal reliability values264. In one approach, the vertical processing and horizontal processingmay be performed in parallel and the resulting non-obstacle estimations242, 258 merged. In another approach, the vertical processing andhorizontal processing may be performed sequentially and the resultingnon-obstacle estimations merged.

For the parallel processing approach, both the vertical processing andthe horizontal processing may be performed in parallel. For example, thevertical estimation module 220 may perform vertical processing and thehorizontal estimation module 222 may simultaneously perform horizontalprocessing as described above. The estimation combiner 224 may generatethe non-obstacle map 226 by identifying each pixel 232 as a non-obstaclearea 266 or obstacle area 268. This identification may be based on thevertical reliability values 250 of the vertical reliability map 248 andthe horizontal reliability values 264 of the horizontal reliability map262.

If both the vertical reliability map 248 and the horizontal reliabilitymap 262 indicate that a pixel 232 has a high reliability, then the pixel232 may be identified as a non-obstacle area 266 in the non-obstacle map226. In other words, a given pixel 232 may be identified as anon-obstacle area 266 in the non-obstacle map 226 where both thevertical reliability map 248 and the horizontal reliability map 262 arecharacterized by a high reliability value for the given pixel 232.

In an implementation, this may be achieved by thresholding the verticalreliability map 248 and the horizontal reliability map 262 with arespective threshold. For example, if a vertical reliability value 250and a horizontal reliability value 264 of a given pixel 232 are greaterthan a threshold, then the pixel 232 may be identified as a non-obstaclearea 266. In the case of both the vertical reliability map 248 and thehorizontal reliability map 262 indicating low reliability, the pixel 232may be labeled as an obstacle area 268.

For the case where either of the vertical reliability map 248 or thehorizontal reliability map 262 indicates a high reliability and theother indicates a low reliability, one or more different mergingapproaches may be performed. In one approach, if at least one of thevertical reliability map 248 or the horizontal reliability map 262indicates that the given pixel 232 has a low reliability, then the givenpixel 232 is identified as an obstacle area 268 in the non-obstacle map226. In other words, a given pixel 232 may be identified as an obstaclearea 268 in the non-obstacle map 226 where at least one of the verticalreliability map 248 or the horizontal reliability map 262 arecharacterized by a low reliability value for the given pixel 232. Inthis approach, if one of the reliability maps 248, 262 is indicating lowreliability, the pixel 232 may be labeled based only on that reliabilitymap 248, 262.

In another approach, the vertical reliability map 248 and the horizontalreliability map 262 are characterized by different reliability values250, 264 for a given pixel 232. In this case, the given pixel 232 may beidentified as a non-obstacle area 266 or obstacle area 268 based on thecoordinate of the given pixel 232. In this approach, if both reliabilitymaps 248, 262 are close to each other in terms of reliability (e.g., onebeing higher than other), the decision can be further enhanced byconsidering the pixel 232 coordinates.

In an example, RHij is the horizontal reliability value 264 for ith andjth pixel 232 from horizontal processing, and RVij is the verticalreliability value 250 from vertical processing (where both reliabilityvalues 250, 264 are normalized if obtained by different methods). If|RHij−RVij|<TH, then this will indicate closeness of reliability maps248, 262. Furthermore, RHij could be compared to RVi+n,j+n, where n is<N (for e.g. N=5 for 720p resolution).

For the sequential processing approach, either vertical processing orhorizontal processing may be performed first. After the first processingis performed, the reliability map (e.g., either the vertical reliabilitymap 248 or the horizontal reliability map 262) may be calculated forwhole depth map 230. For pixels 232 where reliability is not met, asecond processing is called. The results of the first and secondprocessing may be merged to obtain the non-obstacle map 226. An exampleof sequential processing starting with vertical processing is describedin connection with FIG. 9. An example of sequential processing startingwith horizontal processing is described in connection with FIG. 10.

The non-obstacle determination module 216 may perform additionalprocessing of the non-obstacle map 226. In an implementation, thenon-obstacle determination module 216 may perform outlier removal. Forexample, a small number of pixels 232 may be incorrectly labeledobstacle area 268 within a non-obstacle area 266. The non-obstacledetermination module 216 may identify these outliers and change theirstatus to either non-obstacle of obstacle. The non-obstacledetermination module 216 may perform additional filtering of thenon-obstacle map 226 to prepare it for subsequent operations.

The non-obstacle map 226 may be used for different operations. In oneimplementation, the non-obstacle map 226 may be used to identify aregion of interest used by an object detection algorithm. This may beaccomplished as described in connection with FIG. 14. In anotherimplementation, the non-obstacle map 226 may be used to identify aregion of interest used by a lane detection algorithm. This may beaccomplished as described in connection with FIG. 15.

As illustrated in FIG. 2, one or more of the illustrated components maybe optionally implemented by a processor 228. For example, thenon-obstacle determination module 216 may be implemented by a processor228. In some configurations, different processors may be used toimplement different components (e.g., one processor may implement thedepth map generator 218, another processor may be used to implement thevertical estimation module 220, another processor may be used toimplement the horizontal estimation module 222 and yet another processormay be used to implement the estimation combiner 224).

It should be noted that it is possible that to rotate the image 214 andperform diagonal processing. If an image 214 is rotated, the verticaland horizontal processing may become diagonal processing. Therefore,while the terms vertical and horizontal processing are used herein, itmay be recognized that processing could be applied across a differentsection through the rotated image with corresponding coordinate mapping.

FIG. 3 is a flow diagram illustrating a method 300 for performingnon-obstacle area detection. The method 300 may be implemented by, withreference to FIG. 1, an electronic device 102, e.g., a non-obstacledetermination module 116. The electronic device 102 may obtain 302 adepth map 230 from one or more images 214. In one implementation, theelectronic device 102 may generate the depth map 230 from a stereo imagepair. In another implementation, the electronic device 102 may generatethe depth map 230 from a video mono image. Each pixel 232 in the depthmap 230 may have a depth value 234.

The electronic device 102 may perform 304 vertical processing of thedepth map 230 to determine a vertical non-obstacle estimation 242. Thevertical processing may include dividing the depth map 230 into segments236. Each segment 236 may include a number of pixels 232 in a column.The electronic device 102 may estimate linear model parameters 238 foreach segment 236 to determine the vertical non-obstacle estimation 242.The electronic device 102 may determine a vertical reliability map 248that includes a reliability value 250 for the vertical non-obstacleestimation 242.

The electronic device 102 may perform 306 horizontal processing of thedepth map 230 to determine a horizontal non-obstacle estimation 258. Thehorizontal processing may include obtaining a depth histogram 252 foreach row of pixels 232 of the depth map 230. The electronic device 102may determine a terrain line 254 from the depth histogram 252. Theelectronic device 102 may determine the horizontal non-obstacleestimation 258 for each pixel 232 based on the distance of the depthvalue 234 of each pixel 232 from the terrain line 254. The electronicdevice 102 may also determine a horizontal reliability map 262 thatincludes a reliability value 264 for the horizontal non-obstacleestimation 258 of each pixel 232.

The electronic device 102 may combine 308 the vertical non-obstacleestimation 242 and the horizontal non-obstacle estimation 258 todetermine a non-obstacle map 226. In one implementation, the electronicdevice 102 may perform parallel vertical processing and horizontalprocessing. In this implementation, the electronic device 102 maycombine the vertical non-obstacle estimation 242 and the horizontalnon-obstacle estimation 258, as described in connection with FIG. 10. Inanother implementation, the electronic device 102 may perform sequentialprocessing starting with vertical processing, as described in connectionwith FIGS. 11A-11B. In yet another implementation, the electronic device102 may perform sequential processing starting with horizontalprocessing, as described in connection with FIGS. 12A-12B.

FIGS. 4A-4D illustrate an example of a vertical processing of a depthmap 430. The depth map 430 may be generated from one or more images 404.The depth map 430 may include locations corresponding to the pixels ofthe original image(s) 404. Each location (e.g., coordinate) in the depthmap 430 may have a horizontal coordinate 472 and a vertical coordinate474 a corresponding to the pixels of the original image(s) 404.

The depth map 430 may match the size of the original image 404 if thedepth is calculated from the original resolution. In the visualizationof a depth map 430, the depth value 434 may be obtained from thecorresponding coordinates of the original image 404. It should be notedthat a visualization of the depth map 430 may or may not be generatedwhen generating a non-obstacle map 226.

Each pixel 232 of the depth map 430 may have a depth value 434. In thisexample, the depth values 434 a may range from 0 to 120, where 0indicates the farthest distance and 120 indicates the nearest distance.It should be noted that different values may be used for the depthvalues 434.

In an implementation, the disparity to depth mapping can be explained bythe following equations. Z=fBld, where Z is the distance along thecamera Z axis, f is the focal length (in pixels), B is the baseline (inmeters), d is the disparity (in pixels). When the disparity is small,the depth is large. The depth can be found in terms of meters.

It should be noted that depth estimation algorithms from stereo images404 may not be completely reliable. For example, a depth estimationalgorithm might not be able to assign depth values 434 in certainregions. A depth filling algorithm may be used to fill in the missingdepth values 434. For example the neighboring depth value 434 may bepropagated to an adjoining area. The propagation may be from left orfrom right. Alternatively, missing depth values 434 may be filled byinterpolating between left and right, as well as top and bottom.

This example shows a first column 470 a and a second column 470 b. In afirst column graph 475 a, the depth values 434 b are shown for thepixels 232 in the first column 470 a. In this first column graph 475 a,the depth values 434 b are plotted verses the vertical coordinates 474 bof the pixels 232 from the first column 470 a. It should be noted thatin this example, the zero values from 0 to 40 and after 400 pixels isbecause there is no depth assigned there. Because stereo images may becalibrated, they may not have the matching field of view (FOV) aftercalibration. Depth estimation may only be performed only within sameFOV.

In a second column graph 475 b, the depth values 434 c are shown for thepixels 232 in the second column 470 b. In this second column graph 475b, the depth values 434 c are plotted verses the vertical coordinates474 c of the pixels 232 from the second column 470 b. The dashed linefrom 0 to 150 is the mapping showing the algorithm result. The mappingmay only be to sloped (i.e., slanted) lines (which specify a non-objectroad area). To map to objects (which are flat lines), then the samealgorithm may be used, but the mapping parameters may be changed.

The solid line in the graphs 475 a,b comes from original depth values.The dashed line is generated according to the described systems andmethods. It should be noted that the mapping may be based on the slopeof the dashed line. In these graphs 475 a,b, a slope greater than 0indicates a non-obstacle area 266. A slope less than or equal to 0indicates an obstacle area 268.

During vertical processing, the electronic device 102 may divide thedepth map 430 into segments 236. Each segment 236 may include a numberof pixels 232 in a column 470. For example, the segment 236 may be avector of 10-15 pixels 232. The electronic device 102 may determine avertical non-obstacle estimation 242 and may estimate linear modelparameters 238 as described in connection with FIG. 2. If the estimationerror is less than a threshold 246 and the linear fit has a certainslope, then the segment 236 may be labelled as a valid non-obstacle area266.

FIG. 5 is a flow diagram illustrating a method 500 for performingnon-obstacle area detection using vertical processing. The method 500may be implemented by an electronic device 102, e.g., a non-obstacledetermination module 116. The electronic device 102 may divide 502 adepth map 430 into segments 236. Each segment 236 may include a number nof pixels 232 in a column 470. In an implementation, n may equal 10pixels 232.

The electronic device 102 may estimate 504 linear model parameters 238for each segment 236 to determine the vertical non-obstacle estimation242. This may be accomplished according to Equations 1-4 describedabove.

The electronic device 102 may determine 506 a vertical reliability map248 that includes a reliability value 250 for the vertical non-obstacleestimation 242. For example, the electronic device 102 may determine asegment fitting error 244 for each segment 236 based on the differencebetween the estimated linear model parameters 238 and pre-determinedlinear model parameters 240. The segment fitting error 244 may bedetermined according to Equations 5-7 above. The pre-determined linearmodel parameters 240 may be selected from among a plurality of roadcondition models (e.g., flat, hill, valley). Each of the plurality ofroad condition models may have a corresponding set of linear modelparameters 240.

The electronic device 102 may determine the reliability value 250 foreach segment 236 by comparing the segment fitting error 244 of eachsegment 236 to a vertical estimation threshold 246. A given segment 236may have a high reliability value 250 when the segment fitting error 244for the given segment 236 is less than the vertical estimation threshold246.

The electronic device 102 may apply the reliability value 250 for agiven segment 236 to each pixel 232 in the given segment 236. Therefore,each pixel 232 in the vertical reliability map 248 may have areliability value 250 indicating confidence in the vertical non-obstacleestimation 242.

FIGS. 6A-6D illustrate an example of a horizontal processing of a depthmap 630. The depth map 630 may be generated from one or more images 114.The depth map 630 illustrated in FIG. 6A is the same depth map 430described in connection with FIG. 4B (compressed to fit the page). Thedepth map 630 may include locations corresponding to the pixels of theoriginal image(s) 114. Each location in the depth map 630 may have ahorizontal coordinate 672 and a vertical coordinate 674 corresponding tothe pixels 232 of the original image(s) 114.

The electronic device 102 may generate a depth histogram 652. The depthhistogram 652 may be generated by obtaining a histogram of depths foreach row in the depth map 630. For example, the depth histogram 652 maybe generated by projecting the depth map 630 in the vertical axis.

A depth histogram 652 may be calculated for each row in an image 114.The number of bins in the depth histogram 652 corresponds to the numberof maximum disparity values in the image 114. For example, it could be150 for a 720p image, or it could be larger for a larger resolutionimage and smaller for a smaller resolution image. The x axis in theright side of FIG. 6B corresponds to disparity values, and the intensityvalue in the image 114 corresponds to the histogram count for thatdisparity value. The darker the shade it is, the higher the disparityvalue, the lighter the shade it is, the lower the disparity value ofhistogram. In this example, a maximum number of disparity bins is 150.In the depth histogram 652, disparity values are plotted versus thevertical coordinates 674.

The electronic device 102 may apply a Hough transform on the depthhistogram 652 to extract line segments 676. The Hough transform mayextract multiple line segments 676 from the depth histogram 252. Forexample, the Hough transform may generate 10-20 small line segments 676.

The line segments 676 may be merged to form the terrain line 654. Thex-coordinate of the end points of the terrain line 254 determine theclosest and farthest free space distance. In an implementation, theangle of the terrain line 254 may be limited (e.g., between −20 degreesand −40 degrees) as non-obstacle space has certain slopecharacteristics.

If the depth histogram 652 gives a line with a negative slope, itcorresponds to a road segment in front of the car without any obstacles.An obstacle will be represented by a straight vertical line at 90degrees. The terrain line 254 indicates that the depth values in frontof the camera are slowly decreasing as the depth values are analyzed inrows of an image 114. If there is an object, then the object will havesame depth value for a certain number of rows. In this case, depthvalues will not be decreasing. The slope (i.e., angle of the terrainline 254) may be chosen according to the maximum slope a road can have.

The electronic device 102 may label the pixels 232 with depth values 634that lie within a horizontal estimation threshold 260 of the terrainline 654 as non-obstacle areas 266. Those pixels 232 with depth values634 that lie outside the horizontal estimation threshold 260 of theterrain line 654 may be labeled as obstacle areas 268.

FIG. 7 is a flow diagram illustrating a method 700 for performingnon-obstacle area detection using horizontal processing. The method 700may be implemented by an electronic device 102, e.g., a non-obstacledetermination module 116. The electronic device 102 may obtain 702 adepth histogram 252 for each row of pixels 232 of the depth map 230.This may be accomplished as described in connection with FIGS. 6A-6D.

The electronic device 102 may determine 704 a terrain line 254 from thedepth histogram 252. This may be accomplished as described in connectionwith FIGS. 6A-6D.

The electronic device 102 may determine 706 a horizontal non-obstacleestimation 258 for each pixel 232 based on the distance of the depthvalue 234 of each pixel 232 from the terrain line 254. Those pixels 232with depth values 634 that lie within a horizontal estimation threshold260 of the terrain line 654 may be labeled as non-obstacle areas 266.Those pixels 232 with depth values 634 that lie outside the horizontalestimation threshold 260 of the terrain line 654 may be labeled asobstacle areas 268.

The electronic device 102 may determine 708 a horizontal reliability map262 that includes a reliability value 264 for the horizontalnon-obstacle estimation 258 of each pixel 232. For example, the distanceto the mode 256 of the depth histogram 252 may be used as a reliabilitymetric for the horizontal processing. The electronic device 102 maydetermine whether the depth value 234 of each pixel 232 is within areliability threshold 261 of the mode 256 of the depth histogram 252. Agiven pixel 232 may have a high reliability value 264 when the depthvalue 234 of the given pixel 232 is within the reliability threshold 261of the mode 256 of the depth histogram 252.

FIG. 8 is a flow diagram illustrating a method 800 for combining avertical non-obstacle estimation 242 and a horizontal non-obstacleestimation 258 to determine a non-obstacle map 226. The method 800 maybe implemented by an electronic device 102, e.g., a non-obstacledetermination module 116. In this method 800, the electronic device 102may perform vertical processing and horizontal processing in parallel.

The electronic device 102 may perform 802 vertical processing of a depthmap 230 to determine a vertical non-obstacle estimation 242. This may beaccomplished as described in connection with FIG. 5.

The electronic device 102 may obtain 804 a vertical reliability map 248based on a model fitting confidence. This may be accomplished asdescribed in connection with FIG. 5.

The electronic device 102 may perform 806 horizontal processing of thedepth map 230 to determine a horizontal non-obstacle estimation 258.This may be accomplished as described in connection with FIG. 7.

The electronic device 102 may obtain 808 a horizontal reliability map262 based on a depth histogram 252 distance. This may be accomplished asdescribed in connection with FIG. 7.

It should be noted that steps 802, 804, 806, 808 may be performed inparallel. For example, while the electronic device 102 performs thevertical processing steps 802 and 804, the electronic device 102 maysimultaneously perform the horizontal processing steps 806 and 808.

The electronic device 102 may merge 810 the vertical non-obstacleestimation 242 and the horizontal non-obstacle estimation 258 to obtainthe non-obstacle map 226. This merge may be based on the verticalreliability map 248 and the horizontal reliability map 262.

If both the vertical reliability map 248 and the horizontal reliabilitymap 262 indicate that a pixel 232 has a high reliability, then the pixel232 may be identified as a non-obstacle area 266 in the non-obstacle map226. For example, if the vertical reliability map 248 indicates a highvertical reliability value 250 and the horizontal reliability map 262indicates a high horizontal reliability value 264, then the pixel 232may be labeled as a non-obstacle area 266 in the non-obstacle map 226.

For the case where either of the vertical reliability map 248 or thehorizontal reliability map 262 indicates a high reliability and theother indicates a low reliability, one or more different mergingapproaches may be performed. In one approach, if at least one of thevertical reliability map 248 or the horizontal reliability map 262indicates that the given pixel 232 has a low reliability value 250, 264,then the given pixel 232 is identified as an obstacle area 268 in thenon-obstacle map 226. In another approach, the vertical reliability map248 and the horizontal reliability map 262 may indicate differentreliability values 250, 264. In this case, the given pixel 2332 may beidentified as a non-obstacle area 266 or obstacle area 268 based on thecoordinate of the given pixel 232.

FIG. 9 is a flow diagram illustrating another method 900 for combining avertical non-obstacle estimation 242 and a horizontal non-obstacleestimation 258 to determine a non-obstacle map 226. The method 900 maybe implemented by an electronic device 102, e.g., a non-obstacledetermination module 116. In this method 900, the electronic device 102may perform a sequential application of vertical processing followed byhorizontal processing.

The electronic device 102 may perform 902 vertical processing of a depthmap 230 to determine a vertical non-obstacle estimation 242. This may beaccomplished as described in connection with FIG. 5.

The electronic device 102 may obtain 904 a vertical reliability map 248based on a model fitting confidence. This may be accomplished asdescribed in connection with FIG. 5.

The electronic device 102 may identify 906 reliable regions asnon-obstacle areas 266 based on the vertical reliability map 248. Forexample, each pixel 232 that has a high vertical reliability value 250in the vertical reliability map 248 may be labeled as a non-obstaclearea 266 in the non-obstacle map 226.

The electronic device 102 may perform 908 horizontal processing onunreliable regions of the vertical reliability map 248 to determinewhether the unreliable regions are non-obstacle areas 266. For example,the electronic device 102 may perform horizontal processing on at leastone row of pixels 232 that have a low reliability value 250. It shouldbe noted that the length of a row could be as wide as the image 114 orcould be shorter than the image width. The horizontal processing may beaccomplished as described in connection with FIG. 7 to determine ahorizontal non-obstacle estimation 258. The non-obstacle map 226 may beupdated based on the results of the horizontal reliability map 262 ofthe unreliable regions.

FIG. 10 is a flow diagram illustrating yet another method 1000 forcombining a vertical non-obstacle estimation 242 and a horizontalnon-obstacle estimation 258 to determine a non-obstacle map 226. Themethod 1000 may be implemented by an electronic device 102, e.g., anon-obstacle determination module 116. In this method 1000, theelectronic device 102 may perform a sequential application of horizontalprocessing followed by vertical processing.

The electronic device 102 may perform 1002 horizontal processing of adepth map 230 to determine a horizontal non-obstacle estimation 258.This may be accomplished as described in connection with FIG. 7.

The electronic device 102 may obtain 1004 a horizontal reliability map262 based on a depth histogram 252 distance. This may be accomplished asdescribed in connection with FIG. 7.

The electronic device 102 may identify 1006 reliable regions asnon-obstacle areas 266 based on the horizontal reliability map 262. Forexample, each pixel 232 that has a high horizontal reliability value 264in the horizontal reliability map 262 may be labeled as a non-obstaclearea 266 in the non-obstacle map 226.

The electronic device 102 may perform 1008 vertical processing onunreliable regions of the horizontal reliability map 262 to determinewhether the unreliable regions are non-obstacle areas 266. For example,the electronic device 102 may perform vertical processing on one or morecolumn(s) of pixels 232 that have a low horizontal reliability value264. The one or more column(s) of pixels 232 may be as tall as the image114 or could be less than the image height.

The vertical processing may be accomplished as described in connectionwith FIG. 5 to determine a vertical non-obstacle estimation 242. Thenon-obstacle map 226 may be updated based on the results of the verticalreliability map 248 of the unreliable regions.

FIGS. 11A-11B illustrate an example of determining non-obstacle areas1166 in an image 1114. In this example, the original image 1114 a is animage of a one-way road.

The processed image 1114 b shows the non-obstacle area 1166 that isdetermined according to the systems and methods described herein. Theprocessed image 1114 b shows an example from an intermediate step of thealgorithm, not the final result. This is to show there could be holes inthe non-obstacle map 126 that should be further processed. Also thisillustrates the result from vertical processing alone.

The non-obstacle area 1166 includes the open area corresponding to theroad. Certain outlier areas 1178 in non-obstacle area 1166 were notidentified as part of the non-obstacle area 1166. As used herein,outlier means that the depth values in the image 1114 b did not fit inthe predetermined model. This may be due to a possible wrong estimationof depth values (e.g., input depth values may not be perfect), so someareas appear as holes (or outliers). These outlier areas 1178 may beremoved by performing additional processing of the image 1114.

FIGS. 12A-12B illustrate another example of determining non-obstacleareas 1266 in an image 1214. In this example, the original image 1214 ais an image of an intersection. The processed image 1214 b shows thenon-obstacle area 1266 that is determined according to the systems andmethods described herein.

FIGS. 13A-13B illustrate an example of determining distance 1380 from anon-obstacle area 1366. This example shows the processed image 1314 ofthe road described in connection with FIGS. 11A-11B. The non-obstaclearea 1366 is determined according to the systems and methods describedherein.

In the depth map 1330 of FIG. 13B, the x and y coordinates are thecoordinate positions and the shading is indicative of depth. In thisexample, the depth map 1330 includes the depth information for thenon-obstacle area 1366. The depth map 1330 indicates how far there willbe free space in front of the camera.

Using the depth values 234 from the depth map 1330, the electronicdevice 102 may determine various distances associated with thenon-obstacle area 1366 b. For example, the electronic device 102 maydetermine that the farthest distance in the non-obstacle area 1366 b isbetween 45-50 meters away from the image sensor 104. Similarly, theelectronic device 102 may determine that the nearest cars areapproximately 11 meters from the image sensor 104.

FIG. 14 illustrates an example of using the non-obstacle map 226 toidentify a region of interest (ROI) 1482 used by an object detectionalgorithm. This example shows the processed image 1414 of the roaddescribed in connection with FIGS. 11A-11B. The non-obstacle area 1466is determined according to the systems and methods described herein.

The electronic device 102 may then identify one or more ROI 1482 for apotential obstacle area 268. For example, the electronic device 102 mayidentify obstacle areas 268 from the non-obstacle map 226. In animplementation, pixels 232 that are not labeled as non-obstacle area1466 may be identified as an obstacle area 268. These obstacle areas 268may be included in one or more ROIs 1482. In this example, theelectronic device 102 identifies four ROIs 1482 a-d as potential objectareas.

The electronic device 102 may run an object detector in the identifiedROIs 1482. For example, the electronic device 102 may detect whether anROI 1482 includes a car, traffic signal, a pedestrian, lanes, curbs,etc.

Identifying the non-obstacle area 1466 first results in a reduced searcharea for the object detection. This may reduce the amount of processingthat is performed for object detection.

FIG. 15 illustrates an example of using the non-obstacle map 226 toidentify a region of interest (ROI) 1582 used by a lane detectionalgorithm. This example shows the processed image 1514 of a road. Thenon-obstacle area 1566 is determined according to the systems andmethods described herein.

The electronic device 102 may then identify one or more ROI 1582 forroad sides and/or curbs. For example, the electronic device 102 mayidentify obstacle areas 268 from the non-obstacle map 226. In thisexample, the electronic device 102 identifies two ROIs 1582 a-b on thesides of the non-obstacle area 1566 as potential road sides and/orcurbs.

The electronic device 102 may run a lane detector in the identified ROIs1582 a-b. Identifying the non-obstacle area 1566 first results in areduced search area for the lane detection. This may reduce the amountof processing that is performed for lane detection.

FIG. 16 is a flow diagram illustrating a method 1600 for classifying aroad based on estimated linear model parameters 238. The method 1600 maybe implemented by an electronic device 102, e.g., a non-obstacledetermination module 116.

The electronic device 102 may obtain 1602 a depth map 230 of a roadimage 114. The depth map 230 may be generated as described in connectionwith FIG. 2.

The electronic device 102 may perform 1604 segment fitting to estimatelinear model parameters 238. The estimated linear model parameters 238may be determined as described in connection with FIG. 2.

The electronic device 102 may classify 1606 the road based on theestimated linear model parameters 238. For example, the electronicdevice 102 may compare the estimated linear model parameters 238 with aplurality of pre-determined linear model parameters 240. As describedabove, the pre-determined linear model parameters 240 may be associatedwith a plurality of road condition models. Each of the plurality of roadcondition models may have a corresponding set of linear model parameters240. Examples of the road condition models include a flat plane, slope(e.g., hill), valley, irregular road, etc.

In an implementation, the pre-determined linear model parameters 240 canbe obtained via training. Pre-labeled test data can be used, wherepre-labeled refers to labeled images 114 and depth maps 230 of flatroads, or irregular roads, etc. With this data the pre-determined linearmodel parameters 240 may be generated.

The electronic device 102 may determine which of the pre-determinedlinear model parameters 240 best fit the estimated linear modelparameters 238. The road may be classified 1606 according to the roadcondition model that best fits the estimated linear model parameters238. Therefore, by classifying 1606 the road, the electronic device 102may determine the type of free space in the road image 114.

FIG. 17 illustrates certain components that may be included within anelectronic device 1702. The electronic device 1702 may be or may beincluded within a camera, video camcorder, digital camera, cellularphone, smart phone, computer (e.g., desktop computer, laptop computer,etc.), tablet device, media player, television, automobile, personalcamera, action camera, surveillance camera, mounted camera, connectedcamera, robot, aircraft, drone, unmanned aerial vehicle (UAV),healthcare equipment, gaming console, personal digital assistants (PDA),set-top box, etc. The electronic device 1702 includes a processor 1728.The processor 1728 may be a general purpose single- or multi-chipmicroprocessor (e.g., an ARM), a special purpose microprocessor (e.g., adigital signal processor (DSP)), a microcontroller, a programmable gatearray, etc. The processor 1728 may be referred to as a centralprocessing unit (CPU). Although just a single processor 1728 is shown inthe electronic device 1702, in an alternative configuration, acombination of processors (e.g., an ARM and DSP) could be used.

The electronic device 1702 also includes memory 1739. The memory 1739may be any electronic component capable of storing electronicinformation. The memory 1739 may be embodied as random access memory(RAM), read-only memory (ROM), magnetic disk storage media, opticalstorage media, flash memory devices in RAM, on-board memory includedwith the processor, EPROM memory, EEPROM memory, registers, and soforth, including combinations thereof.

Data 1721 a and instructions 1741 a may be stored in the memory 1739.The instructions 1741 a may be executable by the processor 1728 toimplement one or more of the methods described herein. Executing theinstructions 1741 a may involve the use of the data that is stored inthe memory 1739. When the processor 1728 executes the instructions 1741,various portions of the instructions 1741 b may be loaded onto theprocessor 1728, and various pieces of data 1721 b may be loaded onto theprocessor 1728.

The electronic device 1702 may also include a transmitter 1725 and areceiver 1727 to allow transmission and reception of signals to and fromthe electronic device 1702. The transmitter 1725 and receiver 1727 maybe collectively referred to as a transceiver 1729. One or multipleantennas 1737 a-b may be electrically coupled to the transceiver 1729.The electronic device 1702 may also include (not shown) multipletransmitters, multiple receivers, multiple transceivers and/oradditional antennas.

The electronic device 1702 may include a digital signal processor (DSP)1731. The electronic device 1702 may also include a communicationsinterface 1733. The communications interface 1733 may allow enable oneor more kinds of input and/or output. For example, the communicationsinterface 1733 may include one or more ports and/or communicationdevices for linking other devices to the electronic device 1702.Additionally or alternatively, the communications interface 1733 mayinclude one or more other interfaces (e.g., touchscreen, keypad,keyboard, microphone, camera, etc.). For example, the communicationinterface 1733 may enable a user to interact with the electronic device1702.

The various components of the electronic device 1702 may be coupledtogether by one or more buses, which may include a power bus, a controlsignal bus, a status signal bus, a data bus, etc. For the sake ofclarity, the various buses are illustrated in FIG. 17 as a bus system1723.

In accordance with the present disclosure, a circuit, in an electronicdevice, may be adapted to obtain a depth map from one or more images.Each pixel in the depth map may have a depth value. The same circuit, adifferent circuit, or a second section of the same or different circuitmay be adapted to perform vertical processing of the depth map todetermine a vertical non-obstacle estimation. The same circuit, adifferent circuit, or a third section of the same or different circuitmay be adapted to perform horizontal processing of the depth map todetermine a horizontal non-obstacle estimation. The same circuit, adifferent circuit, or a fourth section of the same or different circuitmay be adapted to combine the vertical non-obstacle estimation and thehorizontal non-obstacle estimation to determine a non-obstacle map. Inaddition, the same circuit, a different circuit, or a fifth section ofthe same or different circuit may be adapted to control theconfiguration of the circuit(s) or section(s) of circuit(s) that providethe functionality described above.

The term “determining” encompasses a wide variety of actions and,therefore, “determining” can include calculating, computing, processing,deriving, investigating, looking up (e.g., looking up in a table, adatabase or another data structure), ascertaining and the like. Also,“determining” can include receiving (e.g., receiving information),accessing (e.g., accessing data in a memory) and the like. Also,“determining” can include resolving, selecting, choosing, establishingand the like.

The phrase “based on” does not mean “based only on,” unless expresslyspecified otherwise. In other words, the phrase “based on” describesboth “based only on” and “based at least on.”

The term “processor” should be interpreted broadly to encompass ageneral purpose processor, a central processing unit (CPU), amicroprocessor, a digital signal processor (DSP), a controller, amicrocontroller, a state machine, and so forth. Under somecircumstances, a “processor” may refer to an application specificintegrated circuit (ASIC), a programmable logic device (PLD), a fieldprogrammable gate array (FPGA), etc. The term “processor” may refer to acombination of processing devices, e.g., a combination of a DSP and amicroprocessor, a plurality of microprocessors, one or moremicroprocessors in conjunction with a DSP core, or any other suchconfiguration.

The term “memory” should be interpreted broadly to encompass anyelectronic component capable of storing electronic information. The termmemory may refer to various types of processor-readable media such asrandom access memory (RAM), read-only memory (ROM), non-volatile randomaccess memory (NVRAM), programmable read-only memory (PROM), erasableprogrammable read-only memory (EPROM), electrically erasable PROM(EEPROM), flash memory, magnetic or optical data storage, registers,etc. Memory is said to be in electronic communication with a processorif the processor can read information from and/or write information tothe memory. Memory that is integral to a processor is in electroniccommunication with the processor.

The terms “instructions” and “code” should be interpreted broadly toinclude any type of computer-readable statement(s). For example, theterms “instructions” and “code” may refer to one or more programs,routines, sub-routines, functions, procedures, etc. “Instructions” and“code” may comprise a single computer-readable statement or manycomputer-readable statements.

The functions described herein may be implemented in software orfirmware being executed by hardware. The functions may be stored as oneor more instructions on a computer-readable medium. The terms“computer-readable medium” or “computer-program product” refers to anytangible storage medium that can be accessed by a computer or aprocessor. By way of example, and not limitation, a computer-readablemedium may comprise RAM, ROM, EEPROM, CD-ROM or other optical diskstorage, magnetic disk storage or other magnetic storage devices, or anyother medium that can be used to carry or store desired program code inthe form of instructions or data structures and that can be accessed bya computer. Disk and disc, as used herein, includes compact disc (CD),laser disc, optical disc, digital versatile disc (DVD), floppy disk andBlu-ray® disc where disks usually reproduce data magnetically, whilediscs reproduce data optically with lasers. It should be noted that acomputer-readable medium may be tangible and non-transitory. The term“computer-program product” refers to a computing device or processor incombination with code or instructions (e.g., a “program”) that may beexecuted, processed or computed by the computing device or processor. Asused herein, the term “code” may refer to software, instructions, codeor data that is/are executable by a computing device or processor.

Software or instructions may also be transmitted over a transmissionmedium. For example, if the software is transmitted from a website,server, or other remote source using a coaxial cable, fiber optic cable,twisted pair, digital subscriber line (DSL), or wireless technologiessuch as infrared, radio and microwave, then the coaxial cable, fiberoptic cable, twisted pair, DSL, or wireless technologies such asinfrared, radio and microwave are included in the definition oftransmission medium.

The methods disclosed herein comprise one or more steps or actions forachieving the described method. The method steps and/or actions may beinterchanged with one another without departing from the scope of theclaims. In other words, unless a specific order of steps or actions isrequired for proper operation of the method that is being described, theorder and/or use of specific steps and/or actions may be modifiedwithout departing from the scope of the claims.

Further, it should be appreciated that modules and/or other appropriatemeans for performing the methods and techniques described herein, can bedownloaded and/or otherwise obtained by a device. For example, a devicemay be coupled to a server to facilitate the transfer of means forperforming the methods described herein. Alternatively, various methodsdescribed herein can be provided via a storage means (e.g., randomaccess memory (RAM), read-only memory (ROM), a physical storage mediumsuch as a compact disc (CD) or floppy disk, etc.), such that a devicemay obtain the various methods upon coupling or providing the storagemeans to the device.

It is to be understood that the claims are not limited to the preciseconfiguration and components illustrated above. Various modifications,changes and variations may be made in the arrangement, operation anddetails of the systems, methods, and apparatus described herein withoutdeparting from the scope of the claims.

What is claimed is:
 1. A method performed by an electronic device,comprising: generating a depth map of a scene external to a vehicle;performing first processing in a first direction of the depth map todetermine a first non-obstacle estimation of the scene; performingsecond processing in a second direction of the depth map to determine asecond non-obstacle estimation of the scene; combining the firstnon-obstacle estimation and the second non-obstacle estimation todetermine a non-obstacle map of the scene, wherein the combiningcomprises selectively using at least one element selected from the groupconsisting of a first reliability map of the first processing and asecond reliability map of the second processing; and navigating thevehicle using the non-obstacle map.
 2. The method of claim 1, whereinnavigating the vehicle comprises providing at least the non-obstacle mapas input to an advanced driver assistance systems (ADAS) to regulate atleast one of speed or steering of the vehicle.
 3. The method of claim 1,wherein the navigating comprises using the non-obstacle map to identifya region of interest that is used by at least one of an object detectionalgorithm or a lane detection algorithm.
 4. The method of claim 1,wherein the vehicle is an autonomous automobile or aerial drone, whereinthe navigating comprises navigating the autonomous automobile or aerialdrone without human input using the non-obstacle map.
 5. The method ofclaim 1, further comprising generating the depth map using a stereoimage pair that is captured by two cameras attached to the vehicle. 6.The method of claim 1, further comprising generating the depth map usinga sequence of video images that are captured by a camera attached to thevehicle.
 7. The method of claim 1, wherein combining the firstnon-obstacle estimation and the second non-obstacle estimation furthercomprises: performing both the first processing and the secondprocessing in parallel; and merging the first non-obstacle estimationand the second non-obstacle estimation based on the first reliabilitymap and the second reliability map, wherein a given pixel is identifiedas a non-obstacle area in the non-obstacle map by comparing areliability value for the given pixel in the first reliability map andthe second reliability map.
 8. The method of claim 1, wherein combiningthe first non-obstacle estimation and the second non-obstacle estimationcomprises: performing first processing of the depth map; obtaining thefirst reliability map that includes a reliability value for a pluralityof pixels in the first reliability map; determining reliable regionsthat include pixels of the first reliability map that have reliabilityvalues greater than a threshold; determining unreliable regions thatinclude pixels of the first reliability map that do not have reliabilityvalues greater than the threshold; identifying the reliable regions ofthe first reliability map as non-obstacle areas; and performing secondprocessing on the unreliable regions of the first reliability map todetermine whether the unreliable regions are non-obstacle areas.
 9. Themethod of claim 1, further comprising determining the first reliabilitymap, comprising: determining a segment fitting error for a given segmentof pixels in the depth map based on a difference between estimatedlinear model parameters for the given segment and pre-determined linearmodel parameters, wherein the pre-determined linear model parameters areselected from among a plurality of road condition models, wherein eachof the road condition models has a corresponding set of linear modelparameters; determining a reliability value for the given segment bycomparing the segment fitting error to a first estimation threshold; andapplying the reliability value for the given segment to at least onepixel in the given segment.
 10. The method of claim 1, furthercomprising determining the second reliability map, comprising:determining whether the depth value of a given pixel in the depth map iswithin a range of a mode of the depth histogram, wherein the given pixelhas a high reliability value when the depth value of the given pixel iswithin the range of the mode of the depth histogram.
 11. An electronicdevice, comprising: at least one processor; a memory in communicationwith the at least one processor; and instructions stored in the memory,the instructions executable by the at least one processor to: generate adepth map of a scene external to a vehicle; perform first processing ina first direction of the depth map to determine a first non-obstacleestimation of the scene; perform second processing in a second directionof the depth map to determine a second non-obstacle estimation of thescene; combine the first non-obstacle estimation and the secondnon-obstacle estimation to determine a non-obstacle map of the scene,wherein the combining comprises selectively using at least one elementselected from the group consisting of a first reliability map of thefirst processing and a second reliability map of the second processing;and navigate the vehicle using the non-obstacle map.
 12. The electronicdevice of claim 11, wherein the instructions executable by the at leastone processor to navigate the vehicle comprise instructions executableby the at least one processor to provide at least the non-obstacle mapas input to an advanced driver assistance systems (ADAS) to regulate atleast one of speed or steering of the vehicle.
 13. The electronic deviceof claim 11, wherein the instructions executable by the at least oneprocessor to navigate the vehicle comprise instructions executable bythe at least one processor to use the non-obstacle map to identify aregion of interest that is used by at least one of an object detectionalgorithm or a lane detection algorithm.
 14. The electronic device ofclaim 11, wherein the vehicle is an autonomous automobile or aerialdrone, wherein the instructions executable by the at least one processorto navigate the vehicle comprise instructions executable by the at leastone processor to navigate the autonomous automobile or aerial dronewithout human input using the non-obstacle map.
 15. The electronicdevice of claim 11, further comprising a first camera and a secondcamera, wherein the first and second cameras are attached to the vehicleand in communication with the at least one processor, and wherein theinstructions executable by the at least one processor to generate thedepth map comprise instructions executable by the at least one processorto generate the depth map using a stereo image pair captured by thefirst and second cameras.
 16. The electronic device of claim 11, furthercomprising at least one camera attached to the vehicle and incommunication with the at least one processor, and wherein theinstructions executable by the at least one processor to generate thedepth map comprise instructions executable by the at least one processorto generate the depth map using a sequence of video images captured bythe at least one camera.
 17. The electronic device of claim 11, whereinthe instructions executable by the at least one processor to combine thefirst non-obstacle estimation and the second non-obstacle estimationfurther comprises instructions executable by the at least one processorto: perform both the first processing and the second processing inparallel; and merge the first non-obstacle estimation and the secondnon-obstacle estimation based on the first reliability map and thesecond reliability map, wherein a given pixel is identified as anon-obstacle area in the non-obstacle map by comparing a reliabilityvalue for the given pixel in the first reliability map and the secondreliability map.
 18. The electronic device of claim 11, wherein theinstructions executable by the at least one processor to combine thefirst non-obstacle estimation and the second non-obstacle estimationcomprises instructions executable by the at least one processor to:perform first processing of the depth map; obtain the first reliabilitymap that includes a reliability value for a plurality of pixels in thefirst reliability map; determine reliable regions that include pixels ofthe first reliability map that have reliability values greater than athreshold; determine unreliable regions that include pixels of the firstreliability map that do not have reliability values greater than thethreshold; identify the reliable regions of the first reliability map asnon-obstacle areas; and perform second processing on unreliable regionsof the first reliability map to determine whether the unreliable regionsare non-obstacle areas.
 19. The electronic device of claim 11, furthercomprising instructions executable by the at least one processor todetermine the first reliability map, comprising instructions executableby the at least one processor to: determine a segment fitting error fora given segment of pixels in the depth map based on a difference betweenestimated linear model parameters for the given segment andpre-determined linear model parameters, wherein the pre-determinedlinear model parameters are selected from among a plurality of roadcondition models, wherein each of the road condition models has acorresponding set of linear model parameters; determine a reliabilityvalue for the given segment by comparing the segment fitting error to afirst estimation threshold; and apply the reliability value for thegiven segment to at least one pixel in the given segment.
 20. Theelectronic device of claim 11, further comprising instructionsexecutable by the at least one processor to determine the secondreliability map, comprising instructions executable by the at least oneprocessor to: determine whether the depth value of a given pixel in thedepth map is within a range of a mode of the depth histogram, whereinthe given pixel has a high reliability value when the depth value of thegiven pixel is within the range of the mode of the depth histogram. 21.A computer-program product, comprising a non-transitory tangiblecomputer-readable medium having instructions thereon, the instructionscomprising: code for causing an electronic device to generate a depthmap of a scene external to a vehicle; code for causing the electronicdevice to perform first processing in a first direction of the depth mapto determine a first non-obstacle estimation of the scene; code forcausing the electronic device to perform second processing in a seconddirection of the depth map to determine a second non-obstacle estimationof the scene; code for causing the electronic device to combine thefirst non-obstacle estimation and the second non-obstacle estimation todetermine a non-obstacle map of the scene, wherein the combiningcomprises selectively using at least one element selected from the groupconsisting of a first reliability map of the first processing and asecond reliability map of the second processing; and code for causingthe electronic device to navigate the vehicle using the non-obstaclemap.
 22. The computer-program product of claim 21, wherein the code forcausing the electronic device to navigate the vehicle comprises code forcausing the electronic device to provide at least the non-obstacle mapas input to an advanced driver assistance systems (ADAS) to regulate atleast one of speed or steering of the vehicle.
 23. The computer-programproduct of claim 21, wherein the code for causing the electronic deviceto navigate comprises code for causing the electronic device to use thenon-obstacle map to identify a region of interest that is used by atleast one of an object detection algorithm or a lane detectionalgorithm.
 24. The computer-program product of claim 21, wherein thevehicle is an autonomous automobile or aerial drone, wherein the codefor causing the electronic device to navigate comprises code for causingthe electronic device to navigate the autonomous automobile or aerialdrone without human input using the non-obstacle map.
 25. Thecomputer-program product of claim 21, further comprising code forcausing the electronic device to generate the depth map using a stereoimage pair that is captured by two cameras attached to the vehicle. 26.The computer-program product of claim 21, further comprising code forcausing the electronic device to generate the depth map using a sequenceof video images that are captured by a camera attached to the vehicle.27. The computer-program product of claim 21, wherein the code forcausing the electronic device to combine the first non-obstacleestimation and the second non-obstacle estimation further comprises codefor causing the electronic device to: perform both the first processingand the second processing in parallel; and merge the first non-obstacleestimation and the second non-obstacle estimation based on the firstreliability map and the second reliability map, wherein a given pixel isidentified as a non-obstacle area in the non-obstacle map by comparing areliability value for the given pixel in the first reliability map andthe second reliability map.
 28. The computer-program product of claim21, wherein the code for causing the electronic device to combine thefirst non-obstacle estimation and the second non-obstacle estimationcomprises code for causing the electronic device to: perform firstprocessing of the depth map; obtain the first reliability map thatincludes a reliability value for a plurality of pixels in the firstreliability map; determine reliable regions that include pixels of thefirst reliability map that have reliability values greater than athreshold; determine unreliable regions that include pixels of the firstreliability map that do not have reliability values greater than thethreshold; identify the reliable regions of the first reliability map asnon-obstacle areas; and perform second processing on unreliable regionsof the first reliability map to determine whether the unreliable regionsare non-obstacle areas.
 29. The computer-program product of claim 21,further comprising code for causing the electronic device to determiningthe first reliability map, comprising code for causing the electronicdevice to: determine a segment fitting error for a given segment ofpixels in the depth map based on a difference between estimated linearmodel parameters for the given segment and pre-determined linear modelparameters, wherein the pre-determined linear model parameters areselected from among a plurality of road condition models, wherein eachof the road condition models has a corresponding set of linear modelparameters; determine a reliability value for the given segment bycomparing the segment fitting error to a first estimation threshold; andapply the reliability value for the given segment to at least one pixelin the given segment.
 30. The computer-program product of claim 21,further comprising code for causing the electronic device to determinethe second reliability map, comprising code for causing the electronicdevice to: determine whether the depth value of a given pixel in thedepth map is within a range of a mode of the depth histogram, whereinthe given pixel has a high reliability value when the depth value of thegiven pixel is within the range of the mode of the depth histogram.